Load the data …

This also makes the “target” Return and St.dev for the next 12 months

setwd("/Users/rout/Desktop/Dropbox/2019m6-46892-MSBA-fin/RCode/Assign6") 
source("Main.A6.R")

## Loading required package: ggplot2

## Loading required package: grid

## Loading required package: dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

## Loading required package: reshape2

## Loading required package: RColorBrewer

## Loading required package: MASS

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

## Loading required package: glmnet

## Loading required package: Matrix

## Loading required package: foreach

## Loaded glmnet 2.0-5

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

## Warning in FUN(X[[i]], ...): NaNs produced

##  [1] "Fin"   "Telcm" "Rtail" "Util"  "Oil"   "Agric" "Autos" "Whlsl"
##  [9] "Beer"  "Toys"  "Banks"

Make some Forecasts and Some Plots

This is in RunsForClass.R

First – Lets preduct some Expected Returns (12 month horison)

My parameter Interesting is selecting a lambda regularizer parameter. I did this by picking from the st.dev of the forecast. If you set the regularizer to really big, the st.dev of the forecast goes to zero. I am using my prior that the variation in E[R] should not be zero and not too big

ER<-"E[R| date t]"


z<- Forecast(
        D=Data,
        what=c("Market"),
        use=c("CONSTANT","DPratio"),
        Interesting=c(0.5)   
        )

## [1] "R:Market"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="DP Ratio")
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("CONSTANT","DPratio"),
        Interesting=c(0.5)   
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="DP Ratio")
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("CONSTANT","PDratio","gaip","recession","CreditSpread","TermSpreadL","unrate"),
        Interesting=c(0.05)
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="DP Ratio,int rate, unemployment")       
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("inflation","unrate"), # Growth IP 
        Interesting=c(0.05)
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="inflation,unemployment")
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("CONSTANT","recession"), # Growth IP 
        Interesting=c(0.05)
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="Just nber recession {1,0}")
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("FEDFUNDS","TermSpreadS","TermSpreadL", "CreditSpread"), # Growth IP 
        Interesting=c(0.05)
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="just interest rates")
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("gaip","gacr"), # Growth IP 
        Interesting=c(0.1)
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="recent growth")
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=colnames(P[,grepl("ga",colnames(P))]), # Growth IP 
        Interesting=c(0.01)
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="recent growth (many)")  
print(p)

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=colnames(P[,-c(1:3)]), # EVERYTHING
        Interesting=c(0.01)
        )

## [1] "R:Market"
## [1] "R:Fin"
## [1] "R:Telcm"
## [1] "R:Rtail"
## [1] "R:Util"
## [1] "R:Oil"
## [1] "R:Agric"
## [1] "R:Autos"
## [1] "R:Whlsl"
## [1] "R:Beer"
## [1] "R:Toys"
## [1] "R:Banks"

p<-Plot.Forecast(z,clip=c(0.01,.2),target=ER,title="Everything")        
print(p)

Predict Volatility

This is predicting the volatility over the next 12 months. I calcualted this as V= sum(eR^2) - (average eR)^2. then sigma=sqrt(V). there are other ways to define this.

In general volatility is more predictible

VR<-"sigma[R| date t]"

z<- Forecast(
        D=Data,
        what=c("Market"),
        use=c("CONSTANT","DPratio"),
        Interesting=c(0.1,0.25,0.5,0.75,.9),
        LHS="V"
        )

## [1] "V:Market"

Plot.Forecast(z,clip=c(0.01,.25),target=VR,title="DP Ratio (several lambda)",h=sqrt(12),highlight="")

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("CONSTANT","DPratio"),
        Interesting=c(0.5),
        LHS="V"
        )

## [1] "V:Market"
## [1] "V:Fin"
## [1] "V:Telcm"
## [1] "V:Rtail"
## [1] "V:Util"
## [1] "V:Oil"
## [1] "V:Agric"
## [1] "V:Autos"
## [1] "V:Whlsl"
## [1] "V:Beer"
## [1] "V:Toys"
## [1] "V:Banks"

Plot.Forecast(z,clip=c(0.01,.25),target=VR,title="DP Ratio",h=sqrt(12))

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("CONSTANT","recession"), # Growth IP 
        Interesting=c(0.5),
        LHS="V"
        )

## [1] "V:Market"
## [1] "V:Fin"
## [1] "V:Telcm"
## [1] "V:Rtail"
## [1] "V:Util"
## [1] "V:Oil"
## [1] "V:Agric"
## [1] "V:Autos"
## [1] "V:Whlsl"
## [1] "V:Beer"
## [1] "V:Toys"
## [1] "V:Banks"

Plot.Forecast(z,clip=c(0.01,.25),target=VR,title="recession",h=sqrt(12))

z<- Forecast(
        D=Data,
        what=c("Market","Rtail", "Util",  "Oil"),
        use=c("CONSTANT","recession"), # Growth IP 
        Interesting=c(0.5),
        LHS="V"
        )

## [1] "V:Market"
## [1] "V:Rtail"
## [1] "V:Util"
## [1] "V:Oil"

Plot.Forecast(z,clip=c(0.01,.25),target=VR,title="recession",h=sqrt(12))

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=colnames(P[,-c(1:3)]), # EVERYTHING
        Interesting=c(0.01),
        LHS="V"
        )

## [1] "V:Market"
## [1] "V:Fin"
## [1] "V:Telcm"
## [1] "V:Rtail"
## [1] "V:Util"
## [1] "V:Oil"
## [1] "V:Agric"
## [1] "V:Autos"
## [1] "V:Whlsl"
## [1] "V:Beer"
## [1] "V:Toys"
## [1] "V:Banks"

Plot.Forecast(z,clip=c(0.01,.25),target=VR,title="Everything",h=sqrt(12))

SHARPE RATIOS

This is forward looking calculation of: E[R]/Sigma(R) (twelve months) I am not sure that is best measure of sharpe (only 12 months of data)

## SHARPE RATIO
SR<-"Sharpe Ratio (annual)"
z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("CONSTANT","DPratio"),
        Interesting=c(0.5),
        LHS="S"
        )

## [1] "S:Market"
## [1] "S:Fin"
## [1] "S:Telcm"
## [1] "S:Rtail"
## [1] "S:Util"
## [1] "S:Oil"
## [1] "S:Agric"
## [1] "S:Autos"
## [1] "S:Whlsl"
## [1] "S:Beer"
## [1] "S:Toys"
## [1] "S:Banks"

Plot.Forecast(z,clip=c(-0.01,10),target=SR,title="DP Ratio",h=sqrt(12))

z<- Forecast(
        D=Data,
        what=c("Market",Use.Industries),
        use=c("CONSTANT","recession"), # Growth IP 
        Interesting=c(0.5),
        LHS="S"
        )

## [1] "S:Market"
## [1] "S:Fin"
## [1] "S:Telcm"
## [1] "S:Rtail"
## [1] "S:Util"
## [1] "S:Oil"
## [1] "S:Agric"
## [1] "S:Autos"
## [1] "S:Whlsl"
## [1] "S:Beer"
## [1] "S:Toys"
## [1] "S:Banks"

Plot.Forecast(z,clip=c(NA,NA),target=SR,title="recession",h=sqrt(12))